Visual Inspection Setup Using Automated Reference Image Generation
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Solution Overview
Problem
Existing automated visual inspection systems for production lines are expensive, complex to set up, and require expert integration, leading to prolonged downtime and inconsistency with industrial needs for agility and improvement.
Innovation Solution
A system and method that uses machine learning algorithms to analyze defect-free images for reference, providing real-time status updates and user feedback during the setup process, allowing for efficient setup and inspection by streamlining the process through improved user interfaces and dynamic reference image collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom made automated visual inspection systems are used, then defect detection capability is improved, but system complexity and setup time increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically capturing images of defect-free items during the setup stage and preprocessing them to create reference images before actual inspection begins. This preliminary image collection and processing eliminates the need for manual reference image creation by experts, reducing system complexity while maintaining defect detection capability.
Solution Approach 2:
The system serves itself by automatically generating reference images from captured images of defect-free items without requiring external expert intervention. The automated visual inspection system performs the setup tasks that previously required expert integration, thereby reducing both complexity and setup time while preserving detection capability.
2Measurement precision
If expert integration is used for setting up inspection systems, then inspection accuracy is improved, but setup time and cost increase
Solution Approach 1:
The system performs setup tasks autonomously by automatically capturing images during production, processing them to identify defect-free items, and generating reference images without expert intervention. This self-service capability eliminates the time-consuming expert integration process while maintaining inspection accuracy through automated image analysis algorithms.
Solution Approach 2:
The system replaces the mechanical process of expert integration with an automated computational process. Instead of experts manually creating reference images, the system uses image processing algorithms to automatically generate references from captured images, significantly reducing setup time while preserving inspection accuracy.
3Reliability
If comprehensive image analysis is performed during setup, then defect detection reliability is improved, but processing time increases
Solution Approach 1:
The system performs partial action by focusing image analysis specifically on identifying defect-free items and creating reference images, rather than performing comprehensive analysis of all possible defect types during setup. This targeted approach maintains detection reliability for the specific inspection task while reducing overall processing time.
Solution Approach 2:
The system performs necessary image analysis and reference image creation as preliminary actions during the setup stage, so that comprehensive analysis is not needed during actual production inspection. This preliminary processing maintains detection reliability while minimizing processing time during high-volume inspection operations.
Data Source
AI summary
A visual inspection process which includes receiving a plurality of reference images, each of the reference images including a same-type item on an inspection line, comparing the reference images to each other and displaying to a user a status of the visual inspection process based on the comparison of the reference images to each other.